The present invention relates to methods, apparati and systems for characterizing sleep and, more particularly, to methods, apparati and systems for an efficient determination of sleep stages, body positions and/or sleep disorders of a sleeping subject, using only data derived from signals of electrical activity recorded of a chest of a sleeping subject, such as electrocardiogram (ECG) signals, reflecting cardiac electrical activity, and signals inherently associated with ECG signals, reflecting autonomic nervous system activity and electrical activity of muscles, other than the heart muscle itself, present in the chest of the sleeping subject.
The growing interest in sleep and its disorders, including their influence on health, well-being and public safety (such as in car accidents) have caused a continuously increasing need to perform sleep investigations for both research and clinical purposes. Substantial research has been undertaken directed toward understanding the nature of sleep and of sleep disorders. These researches yielded considerable information concerning human patterns of sleep and wakefulness, and of physiological activities occurring during human sleep. In addition, substantial information has been obtained concerning various sleep disorders.
It is common to divide the sleep of a normal healthy individual into a succession of three states of being, known as Wakefulness, Rapid-Eye-Movement (REM) sleep and Non-REM (NREM) sleep. NREM sleep is subdivided into four sleep stages, which are enumerated from Stage1 to Stage-4 according to the increasing threshold to the influence of external stimuli, these stages are also known as the depth of sleep.
NREM and REM sleep alternate throughout the night in cycles: each sleep cycle lasts about 90-120 minutes; normally each cycle starts with NREM followed by REM. The night contains 4-5 sleep cycles, where, within each cycle along the night, the relative duration of REM sleep increases and the relative duration of NREM decreases. Altogether, the period of NREM sleep represents more than 60% of the night sleep and REM around 30%. Normally, REM sleep first occurs about 90 minutes after sleep-onset (beginning of Stage-1), at the end of the first sleep cycle. This first REM period is short and might be easily overlooked. Each subsequent cycle lasts approximately the same time with shorter and lighter stages of NREM and extending REM periods as the night goes on. Thus, towards morning hours sleep becomes lighter (longer stage 2) and individuals dream more (longer REM). A person may complete between four and six cycles in a typical night's sleep. The overall percentage of the duration of NREM stages and the REM stage is typically about 70% of NREM and about 30% of REM in a healthy adult person.
The percentage of REM sleep is highest during infancy and early childhood, drops off during adolescence and young adulthood, and remains stable thereafter. Total sleep time is longest during early infancy (newborns sleep about 18 hour a day) and sleep times decreases gradually to normal adult values, around 8 hours a night. Paradoxically, the sleep needs during adolescence are increased while the social and curricular needs at this age cause sleep deprivation. In the old age sleep needs do not change, however the ability to sleep is somewhat reduced. NREM sleep becomes lighter, REM remains stable at about 25-30% of total sleep time, the sleep latency increases, and generally sleep is more fragmented than in younger individuals. Monitoring an individual's sleep pattern is crucial for diagnosing sleep disorders, follow up results of treatment of sleep disturbances, and conducting research in the field of sleep.
To date, sleep stages are monitored and examined clinically with a polysomnograph (PSG), which provides data regarding the electrical activity of brain, muscles and eye movement during sleep. The PSG data are analyzed according to a gold standard procedure attributed to Rechtschaffen and Kales (R&K) [Rechtschaffen A., Kales A., eds., “A manual of standardized terminology, techniques and scoring system for sleep staging in human subjects”, Washington D.C.: US Government Printing Office, NIH Publication 204, 1968]. The R&K criteria are primarily based on the analysis of three collected bio-signals: (i) electroencephalogram (EEG), (ii) electrooculogram (EOG), and (iii) electromyogram (EMG). The standard procedure is as follows: EEG signals are derived primarily from the cortex of the brain. At the same time an EMG signal which monitors muscle activity, generally from one of the muscles of the mandible (submental) is measured, together with left eye and right eye EOG (signals produced by eyeball movements relative to the skull). These EEG, EMG and EOG signals are conventionally recorded on a multi-channel physiological recorder.
The number of physiologic inputs which are required in the PSG procedure may vary. Specifically, the monitored signals include EEG (2-4 leads), EOG (2 leads), EMG (chin and limbs; 1-3 or more leads), airflow, respiratory effort (1-2 leads), oxygen saturation, electrocardiogram (ECG), body position and a microphone. Data is stored during the sleep, and the analysis is typically done off-line, according to the standard R&K criteria.
For Stage-1 sleep, which is often considered to be first in the sequence (in models where waking is not included), there is some slowing in EEG frequency, the brain activity is similar to that of wakefulness, there is a slow rolling eye movements and a certain decrease in EMG amplitude. The eyes are closed during Stage-1 sleep, but if aroused from it, a person may feel as if he or she has not slept. Stage-1 usually lasts a few minutes.
Stage-2 is a period of light NREM sleep during which PSG readings are characteristic. EEG signal displays Sleep spindles and biphasic waves-K complexes, EOG signals shows no eye movements in normal subjects free of pharmacological treatments and the EMG signal amplitude is lower than during wakefulness. K-complexes are spontaneous and can be induced by means of sudden auditory stimuli. The heart rate slows, and body temperature decreases. At this point, the body prepares to enter deep sleep. Stages-1 and -2 are collectively known as Light Sleep (LS).
Stages-3 and -4 are deep sleep stages, with Stage-4 being more intense than Stage-3. These stages are known as Slow-Wave-Sleep (SWS). During SWS, especially during Stage-4, the EEG is characterized by slow waves of high amplitude and pattern synchronization. EOG shows no eye movements and the EMG amplitude is significantly lower than during wakefulness.
REM sleep is distinguishable from NREM sleep by changes in physiological states, including its characteristic Rapid-Eye-Movements. However, EEG signal shows wave patterns in REM to be similar to Stage-1 sleep and wakefulness with mixed frequencies and low amplitude desynchronized activity. The eye movements are rapid and similar to the wakefulness eye movements. The skeletal, weight bearing muscles become atonic—the EMG amplitude is extremely low. During normal REM sleep, heart rate and respiration speed up and become erratic, while the face, fingers and legs may twitch. Intense dreaming occurs during REM sleep and there is increased metabolism in certain brain regions. Paradoxically, paralysis occurs simultaneously in the major voluntary muscle groups and the muscles of the upper airways. It is generally thought that REM-associated muscle paralysis is meant to keep the body from acting out the dreams that occur during this stage.
The waking stage is referred to as relaxed wakefulness, during this time period, which varies according to the environmental conditions and individual's characteristics the body prepares for sleep. Normally, as a person becomes sleepier, the body begins to slow down. Muscles begin to relax, and eye movement slows to a roll and the responsiveness to external stimuli decreases steeply with sleep onset.
During sleep the muscles of the upper part of the throat relax. For healthy individual, the upper part of the throat remains open enough to permit the flow of air into the lungs. Some individuals, however, suffer from increased upper airway resistance.
Several sleep disorders and symptoms are associated with increased upper airway resistance, for example, snoring and obstructive apnea. The ability to maintain upper airway patency during the normal respiratory cycle is the result of a delicate equilibrium between the forces that promote airway closure and dilation. Factors predisposing upper airway obstruction include anatomic narrowing, abnormal mechanical linkage between airway dilating muscles and airway walls, muscle weakness, and abnormal neural regulation.
Despite the misleadingly benign clinical presentation, the pathological consequences of sleep apnea, especially in children, may be severe, and some pathological consequences are still being uncovered. Several immediate consequences of upper airway obstruction during sleep are recognized. These include, sleep fragmentation, increased work of breathing, alveolar hypoventilation and intermittent hypoxemia.
Many sleep disorders, in particular snoring, sudden infant death syndrome and obstructive sleep apnea syndrome, are position-dependent. Knowing the body position during sleep is important for study, diagnosis and treatment strategy of such sleep disorders.
Other disorders or disturbances are also related to frequent body position changes during sleep.
Quality of sleep, which is closely related to the amount of body position changes [De Koninck J. et al., “Sleep positions in the young adult and their relationship with the subjective quality of sleep”, 1983, Sleep, 6: 52]. Pulmonary blood flow, which was suggested to be influenced by gravity, and its distribution was shown to depend on body posture [Hakim T. S. et al., “Effect of body posture on spatial distribution of pulmonary blood flow”, J Appl Physiol., 1988, 64(3):1160-70].
Very recently, a connection was found between sleep position and kidney stones [Bijan S., Lu A. F., and Stoller M. L., “Correlation of unilateral urolithiasis with sleep posture” The J. Urol., 2001, 165:1085-1087].
Furthermore, in ST monitoring and other ECG-based applications, where measurements of ECG segments are relevant (e.g. ischemia), movement of the subject is considered artifact [Adams M. G., Drew B. J., “Body position effects on the ECG-implication for ischemia monitoring”, 1997, J electrocard, 30:285-291]. Knowing changes in the body position may be of advantage so as to screen out movement artifacts.
Hence, in addition to the above physiologic inputs, a standard whole night PSG procedure often includes body position monitoring, for example, using specific sensors or visual means, such as a video camera. Determination of body position during sleep may also assists in diagnosing sleep disorders originating from frequent body position changes during sleep.
Whether or not the body position monitoring is included, the PSG procedure is uncomfortable for the subject, artifacts in the acquired signals are very frequent and cause difficulties in data interpretation with the need to redo the study or to increase greatly the time required for the interpretation. Automatic data scoring, although available, is generally not very reliable. Thus, often an expert is reviewing the acquired data and analyses/scores it epoch by epoch. This manual data interpretation is cumbersome and tinted with subjectivity. Standard sleep studies are thus expensive and cumbersome, their reliability is often limited, especially when the data collected is of bad quality and the interpretation is automatic. In addition, the sleep of the subject is influenced by both the requirement to sleep in the laboratory and the multitude of sensors used, which leads to an undesired effect of a measurement influencing the results of the measurement.
It is recognized that sleep is accompanied by cardiocirculatory changes which are a direct consequence of alterations in the autonomic nervous system (ANS). Broadly speaking, during sleep parasympathetic activity is increased while sympathetic activity decreases with phasic activations-deactivations in REM sleep [Parmeggiani P. L. and Morrison A. R., “Alterations in human functions during sleep”, Central Regulation of Autonomic Functions, Lowey A. D. and Spyer K. M., eds., Oxford University Press, 1990, 367].
Recently, analysis of ECG signals in general and Heart-Rate-Variability (HRV) in particular, have been used to quantify the behavior of the ANS, thereby to characterize different sleep stages using different ANS behavior [Berlad I, Shlitner A, Ben-Haim S, Lavie P. “Power spectrum analysis and heart rate variability in stage 4 and REM sleep: Evidence for state specific changes in autonomic dominance”, J. Sleep Res. 1993, 2:88; Baharav et al. “Fluctuations in autonomic nervous activity during sleep displayed by power spectrum analysis of heart rate variability”, Neurology 1995, 45:1183; Bonnet M. H., and Arand, D. L., “Heart rate variability: sleep stage, time of night, and arousal influence”, EEG Cli. Neurophy 1997, 102:390; Scholtz U. J., Bianchi A. M., Cerutti S, Kubicki S., “Vegetative Background of Sleep: Spectral Analysis of the Heart Rate Variability” Physiol Behav, 1997, 62:1037; Baharav A., Shinar Z., Sivan Y., Toledo E., Keselbrener L., and Akselrod S., “Autonomic changes associated with sleep onset investigated by time-frequency decomposition of heart rate variability”, Sleep 1998, 21:208; Monti A, Medigue C, Nedelcoux H, Escourrou P., “Autonomic control of the cardiovascular system during sleep in normal subjects” Eur J Appl Physiol, 2002, 87:174].
The ANS plays a cardinal role in the control of cardiovascular function. Heart rate (HR), heart excitability and contractility are under the constant influence of the parasympathetic-sympathetic balance. Parasympathetic nerves and sympathetic fibers innervate the Sino-Atrial (SA) node; the parasympathetic influence is inhibitory while the sympathetic influence is excitatory. The parasympathetic fibers to the SA node are driven by inhibitory and excitatory inputs from peripheral receptors (baroreceptors, chemoreceptors, cardiac, pulmonary and airway receptors). Behavioral adaptive influence of the heart rate at the sinus node is mediated by supramedullary inputs to the cardiovagal neurons. The origin of the sympathetic innervation of the heart is located at the T2-T5 segment of the spinal cord and the preganglionic fibers synapse in the cervical ganglia; the post synaptic ganglionic fibers innervate the SA node (predominantly Right sympathetics increase HR) as well as the Atrio-Venticular (AV) node (predominantly Left sympathetics—increase AV conduction and cardiac contractility).
Normal cardiac function is regulated by the complex balance of the sympathetic and parasympathetic outflows to the heart. This balance is also responsible for the susceptibility to arrhythmias: while vagal activity has a protective role, sympathetic activity lowers the threshold to ventricular fibrillation. Normal heart function, heart rate included, is modulated by the fluctuations in the sympathetic and parasympathetic flow to the heart. These fluctuations induce beat-to-beat variability in heart rate and arterial pressure. Hence, the analysis of the instantaneous fluctuations in cardiovascular variables supplies valuable information on the autonomic control in an intact organism.
The early methods of analysis of HRV to study the ANS employed algorithms based on Fast-Fourier-Transform (FFT) [Akselrod et al. “Power spectrum analysis of heart rate fluctuations: a quantitative probe of beat to beat cardiovascular control”, Science 1981, 213:220] and autoregressive methods [Malliani et al. “Cardiovascular neural regulation explored in the frequency domain”, Circulation, 1991, 84:482]. These pioneer methods require stationary signals for a relatively long time period, hence allow for estimation of the autonomic function under steady state conditions. However, it has been realized that spreading eventual time-dependent changes in frequency content over the entire time window, results in obscuring any insight into the time axis within the trace length.
To overcome the non physiologic assumption of stationary conditions, new mathematical methods have been developed. Their quantitative description is based on the use of time-frequency spectral decomposition of the simultaneous HR, blood pressure (BP) and respiratory signals. A sequential estimation of power spectra, such as the use of a time shifted short time Fourier transform [Nawab S. H. and Quatieri T. F., “Short-Time Fourier Transform”, Advanced Topics in Signal Processing, Lim and Oppenheim, eds, Englewood Cliffs, N.J., Prentice Hall 1988, 289] represents the most straightforward attempt to overcome this limitation. However it suffers from the intrinsic compromise, which involves its loss in time resolution within the power spectrum of each sub-trace, as well as its severe limitation regarding the minimum frequency it can focus on.
Various approaches have been recently developed in order to overcome these limitations (to this end see, e.g., a review by Cohen L., entitled “Time-frequency distributions” and published in Proc. IEEE 1989, 77:941). These approaches include, Selective Discrete Algorithm (SDA) [Keselbrener L and Akselrod S. “Selective discrete Fourier transform algorithm for time-frequency analysis: Methods and application on simulated and cardiovascular signals” IEEE Trans. Biomed. Eng. 1996, 43:789], modified Wigner-Ville [Novak P and Novak V, “Time-frequency Mapping of the Heart Rate, Blood Pressure and Respiratory Signals”, Medical & Biological Engineering and Computing, 1993, 31:103], time-dependent autoregression [Bianchi et al., “Time-Variant Power Spectrum Analysis for the detection of Transient episode in HRV Signals”, IEEE Transactions on Biomedical Eng., 1993, 40: 136], and Wavelets [Meyer Y., “Wavelets: Algorithms and applications”, Ed. SIAM, Philadelphia 1993].
The above studies were primarily aimed at investigating autonomic activity (in steady and non-steady conditions) using HRV analysis, and when focusing on sleep, previous studies were primarily directed at investigating sleep physiology by means of HRV analysis. However, prior art methods fail to exploit HRV for the purpose of scoring sleep, in general, and determining the various sleep stages in particular.
There is thus a widely recognized need for, and it would be highly advantageous to have, a method, apparatus and system for determining sleep stages of a subject, based on data derived solely from electrical signals recorded of a chest of a sleeping subject, and devoid of the limitations associated with prior art methodologies.